def test_variables_samples(achr: ACHRSampler) -> None:
"""Test variable samples."""
vnames = np.array([v.name for v in achr.model.variables])
s = achr.sample(10, fluxes=False)
assert s.shape == (10, achr.warmup.shape[1])
assert (s.columns == vnames).all()
assert (achr.validate(s) == "v").all()
def test_validate_wrong_sample(achr: ACHRSampler, model: "Model") -> None:
"""Test sample correctness."""
s = achr.sample(10)
s["hello"] = 1
with pytest.raises(ValueError):
achr.validate(s)
def test_complicated_model():
"""Test a complicated model.
Difficult model since the online mean calculation is numerically
unstable so many samples weakly violate the equality constraints.
"""
model = Model('flux_split')
reaction1 = Reaction('V1')
reaction2 = Reaction('V2')
reaction3 = Reaction('V3')
reaction1.bounds = (0, 6)
reaction2.bounds = (0, 8)
reaction3.bounds = (0, 10)
A = Metabolite('A')
reaction1.add_metabolites({A: -1})
reaction2.add_metabolites({A: -1})
reaction3.add_metabolites({A: 1})
model.add_reactions([reaction1, reaction2, reaction3])
optgp = OptGPSampler(model, 1, seed=42)
achr = ACHRSampler(model, seed=42)
optgp_samples = optgp.sample(100)
achr_samples = achr.sample(100)
assert any(optgp_samples.corr().abs() < 1.0)
assert any(achr_samples.corr().abs() < 1.0)
# > 95% are valid
assert sum(optgp.validate(optgp_samples) == "v") > 95
assert sum(achr.validate(achr_samples) == "v") > 95
def test_complicated_model():
"""Test a complicated model.
Difficult model since the online mean calculation is numerically
unstable so many samples weakly violate the equality constraints.
"""
model = Model('flux_split')
reaction1 = Reaction('V1')
reaction2 = Reaction('V2')
reaction3 = Reaction('V3')
reaction1.bounds = (0, 6)
reaction2.bounds = (0, 8)
reaction3.bounds = (0, 10)
A = Metabolite('A')
reaction1.add_metabolites({A: -1})
reaction2.add_metabolites({A: -1})
reaction3.add_metabolites({A: 1})
model.add_reactions([reaction1, reaction2, reaction3])
optgp = OptGPSampler(model, 1, seed=42)
achr = ACHRSampler(model, seed=42)
optgp_samples = optgp.sample(100)
achr_samples = achr.sample(100)
assert any(optgp_samples.corr().abs() < 1.0)
assert any(achr_samples.corr().abs() < 1.0)
# > 95% are valid
assert sum(optgp.validate(optgp_samples) == "v") > 95
assert sum(achr.validate(achr_samples) == "v") > 95
from scipy.stats import hypergeom
modelIN = cobra.io.load_matlab_model('HumanGEMNHBESARS_cons.mat')
modelUIN = cobra.io.load_matlab_model('HumanGEMNHBEMock_cons.mat')
modelIB = cobra.io.load_matlab_model('HumanGEMBiopsySARS.mat')
modelUIB = cobra.io.load_matlab_model('HumanGEMBiopsyMock.mat')
modelIN.reactions.EX_sarscov2s.bounds = (-0.1259, 1000)
modelUIN.reactions.HMR_10024.bounds = (-0.0088, 1000)
achr_IN = ACHRSampler(modelIN, thinning=100)
achr_UIN = ACHRSampler(modelUIN, thinning=100)
achr_IB = ACHRSampler(modelIB, thinning=100)
achr_UIB = ACHRSampler(modelUIB, thinning=100)
samples_IB = achr_IB.sample(10000)
samples_UIB = achr_UIB.sample(10000)
samples_IN = achr_IN.sample(10000)
samples_UIN = achr_UIN.sample(10000)
#KS Test
def bootstrapCI(rxn):
bsci = []
for i in range(1000):
bt_samp = rxn.sample(1000, replace=True)
bsci.append(bt_samp.mean())
ci_low = np.percentile(bsci, 2.5)
ci_high = np.percentile(bsci, 97.5)
if (ci_low > 0 or ci_high < 0):
def test_sampling(achr: ACHRSampler) -> None:
"""Test sampling."""
s = achr.sample(10)
assert all(achr.validate(s) == "v")